Furthermore, BioBooks levels off in performance while Medline has significant improvement left in it.

while the hypothesis seems reasonable, we were unable to make it work.


Unlike CCGs, however, a right corner transformed grammar does not allow backward function application, composition, or raising.

it does not introduce spurious ambiguity between forward and backward operations, but cannot be taken to explicitly encode argument structure, as CCGs can.


These constituents need to be completed, but using the standard annotation there is only one chance to make use of the information about the error that has occurred -the NP → NP PP-UNF rule.

by the time the error section is completed, there is no information by which a parsing algorithm could choose to reduce the topmost NP to EDITED other than independent rule probabilities.


If so, near matches for justification sentences may still be useful in indicating that, at least, the right portion of the document was identified.

to test for near matches, we scored a match if the gold sentence occurred on either side of the system-selected sentence.


Since we are interested not only in learning how to compress, but also when to compress, we also include in this procedure unchanged CFG rule pairs that are attested in the corpus.

different ways of expanding a CFG rule compete with each other as well as the possibility of not doing any expansion.


Since our model is lexicalized, the bigram scores become less relevant, which was confirmed by experimentation during development.

in our implementation we exclude the bigram scores and other related aspects of the algorithm such as pruning of bigram-suboptimal phrases.


Furthermore, the channel model is likely to prefer to leave sentences intact, since that is the most prevalent pattern in the training data.

they argue, the channel model is not really compressing, and it is only by virtue of the length penalty that anything gets shortened at all.


function returns false otherwise, and match a is defined analogously for the pair of ancestors.

the pair of functions match d , match a are "morph or mention" matchers.


We use matching similarity to measure how well the source phrases match each other.

given two source phrases f J 1 and f ′ J 1 , the matching similarity is computed as:   where, partial matching takes full matching (SIM ( f ,f ) = 1.0) as a special case.


Although the matching similarity of the two phrases is only 0.2, as they have the same POS sequence, the word alignments are the same.

the lower source phrase can be translated according to the upper phrase pair with correct word reordering.


Given a source sentence, Moses firstly uses the full matching strategy to search all possible translation options from the phrase table, and then uses a beam-search algorithm for decoding.

we do incorporation by performing partial matching for phrase translation before decoding.


For example, the segmentation and translation for the Chinese sentence "¢ð Ä " are as follows: but | the long-term trend of economic output | will Here the source phrase " Ä " cannot be fully matched.

the decoder breaks it into 4 short phrases, but performs an incorrect reordering.


PropBank (Kingsbury and Palmer, 2002) identifies ARGM-TMP and ARGM-CAU relations, but arguments may only be temporal or causal, never both.

existing corpora are missing some crucial pieces for studying temporal-causal interactions.


Favoring shorter solutions at the expense of some loss in performance is known as parsimony, and it has already been successfully used in genetic programming (Koza, 1992).

the final formula for the fitness function we used incorporates the parsimony factor and is given by where F 1 (j) is the F 1 score (ranging from 0 to 1) of the solution j, η is the parsimony factor, L max is the maximal size (measured in number of nodes), and L(j) is the size of solution j.


Recent studies of natural language parsing have shown a clear and steady shift of focus from pure syntactic analyses to more semantically informed structures.

we have seen an emerging interest in parser evaluation based on more theoryneutral and semantically informed representations, such as dependency structures.


The inference of semantic roles is based on training of BNs by presenting instances of the features extracted, during the learning process.

a training example corresponding to the features shown in Table 2 might be represented as Figure 1 shows, using a first-order approach.


Pairwise comparisons using the Bonferroni method show that all comparisons are statistically significant (at p < .001) except for the one between Generic Intersection and Generic Union.

in particular: Q-based union is ranked significantly higher than Q-based intersection, which in turn is ranked significantly higher than both Generic union and intersection (whose respective ranks are not significantly different).


Here a consistent preference order was found, with rank 1 = Q-based union, rank 2 = Q-based Intersection, rank 3/4 = Generic intersection / union.

: participants clearly prefer Q-based fusions, and prefer more complete answers over shorter ones.


If the syntactic analysis is to guide semantic analysis, it must reflect as closely as possible what the person who produced the sentence was trying to express.

while statistical, data-driven parsing has solved the robustness problem, it is not clear that it is has solved the accurate robustness problem.


Training on a combination of grammatical and ungrammatical material gives the best results for all three development sets.

for the E2 experiments we use the E1mixed parser rather than the E1error parser.


In all our experiments we assume that the same dialog act is shared by a parallel sentence pair.

even though the dialog act prediction is performed for English, we use the predicted dialog act as the dialog act for the source language sentence.


In particular, the size of the support vectors set can grow linearly with the number of training examples, of which there are usually at least tens of thousands.

the computation of the decision function is computationally expensive.


The mapping function φ R (x) zeros out all the elements of x not belonging to F R , while φ C (x) zeroes out all the elements of x not in F C .

for every x: We now rewrite the kernel function: The first 3 terms are non-zero only when at least one rare feature exists.


In contrast, we use a simpler approach to choose which features to include in the expansion, and we also compensate for the feature we did not include by the PKI method.

our method generates smaller expansions while computing the exact decision function and not an approximation of it.


Breaking the reference answer down into fine-grained facets permits a more focused assessment of the student's response, but a simple yes or no entailment at the facet level still lacks semantic expressiveness with regard to the relation between the student's answer and the facet in question, (e.g., did the student contradict the facet or completely fail to address it?)

it is also necessary to break the annotation labels into finer levels in order to specify more clearly the relationship between the student's answer and the reference answer facet.


Interestingly, apart from a few exceptions, the results are rather good for all the pairs of training and test language.

the use of features like frequencies, probabilities or mutual information of compound parts is truly language-independent and the models learned from one language can safely be applied for decompounding a different language without the need of annotating a gold-standard for it.


Sentiment classification is a very domainspecific problem; training a classifier using the data from one domain may fail when testing against data from another.

real application systems usually require some labeled data from multiple domains, guaranteeing an acceptable performance for different domains.


However, the feature-level fusion approach simply pools all training data from different domains and treats them equally.

it can not capture the unbalanced information.


Word prediction offers much more headroom in terms of improvements in keystroke savings.

our ongoing research will focus on word prediction over word completion.


The practical limit is very close to the theoretical limit in the case of Switchboard.

the remaining gap between the practical limit and actual performance must be due to other differences between testing and training data, limitations of the model, and limitations of language modeling.


These corpora define the semantic roles of predicates for each frame independently.

it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances.


Although the role hierarchy was expected to generalize semantic roles, no positive results for role classification have been reported (Baldewein et al., 2004).

the generalization of semantic roles across different frames has been brought up as a critical issue for FrameNet (Gildea and Jurafsky, 2002;Shi and Mihalcea, 2005;Giuglea and Moschitti, 2006) In this paper, we explore several criteria for generalizing semantic roles in FrameNet.


Finally, the Precedes relation describes the sequence of states and events, but does not specify the direction of semantic inclusion relations.

we simply try H child , and H ance y i for this relation type.


Some names are shared by the roles in different frames, whose identifiers are different.

we examine the semantic commonality of these names; we construct an equivalence class of the roles sharing the same name.


The role-group features will receive more training instances by collecting instances for fine-grained roles.

semantic roles with few training instances are expected to receive additional clues from other training instances via role-group features.


Another advantage of this approach is that the usefulness of the different role groups is determined by the training processes in terms of weights of feature functions.

we do not need to assume that we have found the best criterion for grouping roles; we can allow a training process to choose the criterion.


This approach performs similarly to the overwriting approach, and it may obscure the differences among roles.

they only re-used the descriptors as a similarity measure for the roles whose coreness was peripheral.


In our algorithm, the initial set of potential arguments consists of constituents in the Seginer parser's parse tree.

the fraction of arguments that are also constituents (81.87% for English and 51.83% for Spanish) poses an upper bound on our algorithm's recall.


According to Chinese head percolation tables used in the PS to DS conversion tool Penn2Malt 3 and Charniak's parser 4 , the head of VP-2 is the word (a preposition, with BA as its POS tag in CTB), and the head of IP-OBJ is .


However, It is proven to be quite inefficient in computation.


The latter then obtains an opportunity to provide a great deal of useful guideline information to help determine how the former should be tackled.

we have cause for attributing the effectiveness of the proposed method to the similarity of these two treebanks.


Punctuation is often a marker of clause or phrase boundaries.

predicting paired punctuation incorrectly can lead to incorrect parses, as in the following example.


In many domains, interacting with the environment is expensive.

we use two techniques that allow us to take maximum advantage of each environment interaction.


At the same time, there is a correlation between ballstopped and utterances such as pink2 holds onto the ball, which are not aligned to any record in the annotation.

our model incorrectly chooses to align the two.


Suppose we have a unigram word distribution given a word v is different from G 1 , but will be similar to G 1 especially for high frequency words.

we can generate G 2 from a PY process of base measure G 1 , as , and G 1 , G 2 , G 3 will form a tree structure shown in Figure 1(a).


In this representation, each n-gram context h (including the null context for unigrams) is a Chinese restaurant whose customers are the n-gram counts c(w|h) seated over the tables The seatings has been incrementally constructed by choosing the table k for each count in c(w|h) with probability proportional to where c hwk is the number of customers seated at table k thus far and t h• = w t hw is the total number of tables in h. When k = new is selected, t hw is incremented, and this means that the count was actually generated from the shorter context h .

in that case a proxy customer is sent to the parent restaurant and this process will recurse.


However, in our case "words" are not known a priori: the next section describes how to accomplish this by constructing a nested HPYLM of words and characters, with the associated inference algorithm.

far we have assumed that the unigram G 1 is already given, but of course it should also be generated as Here, a problem occurs: What should we use for G 0 , namely the prior probabilities over words 2 ?


Conversely, when a table becomes empty, this means that the data associated with the table are no longer valid.

we remove the corresponding customers from the character HPYLM using the inverse procedure of adding a customer in Section 2.


However, this sampler is too inefficient since time series data such as word segmentation have a very high correlation between neighboring words.

the sampler is extremely slow to converge.


In retrospect, our NPYLM is essentially a hierarchical Markov model where the units (=words) evolve as the Markov process, and each unit has subunits (=characters) that also evolve as the Markov process.

for such languages as English that have already space-separated tokens, we can also begin with tokens besides the character-based approach in Section 5.3.


The second term in the RHS of Equation (21), on the other hand, The monotone convergence theorem justifies the convergence in the second step above.


In both German and Dutch there are only about 50 patterns in 250K examples.

1 for a particular input sequence, we can safely limit our consideration to only the small set of output patterns of the same length.


1 Therefore, for a particular input sequence, we can safely limit our consideration to only the small set of output patterns of the same length.

unlike typical sequence predictors, we do not have to search for the highest-scoring output according to our model.


In a ranking framework, we can score output sequences using their full output pattern.

we can easily learn the rules in languages with regular stress rules.


The general tree-growing algorithm has no notion of the letter distance, but instead chooses the letters on the ba-sis of their estimated information gain (Manning and Schütze, 1999).

it will sometimes query a letter at position +3 (denoted l 3 ), for example, before examining the letters that are closer to the center of the context window.


We have manually verified and corrected around 240,000 pairs to clean up the corpus.

we arrive at a set of 560,768 English-Chinese (EC) pairs that follow the Chinese phonetic rules, and a set of 83,403 English-Japanese Kanji (EJ) pairs, which follow the Japanese phonetic rules, and the rest 29,219 pairs (REST) being labeled as incorrect transliterations.


German and Dutch need more advanced methods than suffix replacement since their affixing of words (inflection of words) can include both prefixing, infixing and suffixing.

we created a trainable lemmatizer that handles pre-and infixes in addition to suffixes.


There is no preferential order between the children of a rule and all paths away from the root must be regarded as equally valid.

the DAG may lead to several lemmas for the same input word.


Although these changes are correlated by the inflectional rules of the language, the number of combinations is still large, while at the same time the number of training examples exhibiting such combinations is relatively small.

the more complex rules involving infix or prefix subpatterns or combinations thereof are less well-founded than the simple suffix-only rules.


The suffixonly algorithm is not able to generalize over training examples that do not fulfil this condition and generates many rules based on very few examples.

everything else being equal, the set of suffix-only rules generated by the affix algorithm must be of higher quality than the set of rules generated by the suffix algorithm.


Since the source-pivot and pivot-target parallel corpora are independent, the pivot sentences in the two corpora are distinct from each other.

some linguistic phenomena in the source-pivot corpus will lost if they do not exist in the pivottarget corpus, and vice versa.


In addition, we found out that the grow heuristic performed the best for all the systems.

for an individual system, we used the translation results produced using the grow alignment heuristic.


When the arity of the edge is 2, a rule has the general form aX 1 bX 2 c, where X 1 and X 2 are sequences from tail nodes.

we need to consider all new sequences which can be created by the cross-product of the n-grams on the two tail nodes.


We hypothesize that the default MBR parameters (Tromble et al., 2008) are well tuned.

there is little gain by additional tuning using MERT.


The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models.

it can not only utilize forest structure that compactly encodes exponential number of parse trees but also capture nonsyntactic translation equivalences with linguistically structured information through tree sequence.


Fundamentally, syntax-based SMT views translation as a structural transformation process.

structure divergence and parse errors are two of the major issues that may largely compromise the performance of syntax-based SMT (Zhang et al., 2008a;.


In nature, the two methods would be complementary to each other since they manage to solve the negative impacts of monolingual parse errors and cross-lingual structure divergence on translation results from different viewpoints.

one natural way is to combine the strengths of the two modeling methods for better performance of syntax-based SMT.


For any given phrase in a sentence, there is at least one tree sequence covering it.

the tree sequence-based model has great potential to address the structure divergence issue by using tree sequence-based non-syntactic translation rules.


To address this issue, we introduce the concepts of virtual node and virtual hyperedge to convert a complete parse forest to a non-complete forest which is designed to encode all the tree sequences that we want.

by doing so, the tree sequence rules can be extracted from a forest in the following two steps: 1) Convert the complete parse forest into a non-complete forest in order to cover those tree sequences that cannot be covered by a single tree node.


When we build multiple MT systems from multiple source languages to the new target language, each MT system can be seen as a different 'view' on the desired output translation.

we can train our multiple MT systems using either self-training or co-training (Blum and Mitchell, 1998).


However, manual evaluation of a large number of documents necessary for a relatively unbiased view is often unfeasible, especially in the contexts where repeated evaluations are needed.

there is a great need for reliable automatic metrics that can perform evaluation in a fast and consistent manner.


In general, the guideline for composing wiki articles is to avoid overlong and over-complicated styles.

the strategy of 'split it' into a series of articles is recommended; so wiki articles are usually not too long and contain limited number of sentences.


However, the principle of typical extractive summarization approaches is that the sentences whose contents are repeatedly emphasized by the authors are most important and should be included (Silber and McCoy, 2002).

it is challenging to summarize wiki articles due to low redundancy (and links) between sentences.


To overcome this problem, we seek (i) more reliable links between passages, (ii) appropriate weighting metric to emphasize the salient concepts about the topic, and (iii) additional guideline on utilizing non-textual features such as outline and infobox.

we develop wiki concepts to replace 'bag-of-words' approach for better link measurements between sentences, and extend an existing summarization model on free text to integrate structural information.


In addition, we ignore all close-class terms, since we cannot find efficient method to infer reliable links across them.

texts are represented as a vector of wiki concepts.


Also, if RPs of their covered base nodes changes, the original derived nodes will modify their RPs as well.

the new RPs in derived nodes and based nodes will lead to better priority of ranking derived nodes, which is likely to result in a better summary.


At the core of these methods is the assumption that fragments of text conveying similar information have similar word distribution patterns.

often a simple segment clustering across domain texts can identify strong patterns in content structure (Barzilay and Elhadad, 2003).


Adding articles from systems which have previously demonstrated poor quality would be improper, especially in Diseases.

we present this analysis as an additional observation rather than a rigorous technical study.


For example, both the iPhone and Blackberry have keyboards, but we observed in our corpus that positive opinions about the keyboard are associated with the pro Blackberry stance.

we need to find distinguishing aspects, which the topics may or may not share.


However, opinions toward that aspect might discriminate between them.

the basic unit in our web mining process is a polarity-target pair.


That is, the weights corresponding to the sides w j and u j are interchanged in equation 4.

conceded opinions are effectively made to count towards the opposing side.


The OpTopic system This system considers only explicit mentions of the topic for the opinion analysis.

for this system, the step of opinion-target pairing only finds all topic + 1 , topic − 1 , topic + 2 , topic − 2 instances in the post (where, for example, an instance of topic + 1 is a positive opinion whose target is explicitly topic 1 ).


The "network" aspect shows a comparatively greater relatedness to the blackberry than to the iPhone.

opPMI uses it as a proxy for the Blackberry.


The lexicon is word based, but, as shown by (Wiebe and Mihalcea, 2006;Su and Markert, 2008), many subjective words have both objective and subjective senses.

one major source of errors is a false hit of a word in the lexicon.


We do not exploit comparative constructs, but rather probabilistic associations.

our approach and theirs are complementary.


The labeled English reviews are translated into labeled Chinese reviews, and the unlabeled Chinese reviews are translated into unlabeled English reviews, by using machine translation services.

each review is associated with an English version and a Chinese version.


Each Chinese review was translated into English review, and each English review was translated into Chinese review.

each review has two independent views: English view and Chinese view.


As with F 0 , one can also use soft sentiment labeling for documents, though our experiments are conducted with hard assignments.

the semi-supervised learning with lexical knowledge can be described as Where α > 0, β > 0 are parameters which determine the extent to which we enforce F ≈ F 0 and G ≈ G 0 respectively, C 1 and C 2 are diagonal matrices indicating the entries of F 0 and G 0 that correspond to labeled entities.


( 4) can be solved using the following update rules the algorithm for semi-supervised learning with lexical knowledge based on our matrix factorization framework, referred as SSMFLK, consists of an iterative procedure using the above three rules until convergence.


Empirically, the number of iterations before practical convergence is usually very small (less than 100).

computationally our approach scales to large datasets even though our experiments are run on relatively small-sized datasets.


In this study we use only the subset of PANDICTIONARY that was computed starting from the English Wiktionary senses.

this subsection under-reports PANDICTIONARY's coverage.


Since the optimal term set for a full taxonomy is always C, the only unknown part left is R .

equation (3) can be transformed equivalently into: Note that in the framework, terms are added incrementally into a taxonomy.


First, we show that a machine learner operating under a 0-1 loss minimization principle could sustain a hard case bias of θ( 1 √ N ) in the worst case.

while annotation noise is hazardous for small datasets, it is better tolerated in larger ones.


This implies that a value as low as −2σ cannot be ruled out with high (say 95%) confidence.

an imbalance of up to 2 There are between 0 and λ h about 2 √ c 1 γ2 d more −1 hard instances than +1 hard instances, as opposed to c 1 γ2 d easy instances that are all +1.


For 0 ≤ ψ ≤ T √ d, misclassified hard cases dominate: where Φ −1 (γ) is the inverse of the normal distribution density.

e(ζ 2 ) ≥ min{H T (δ), H γ (δ)}, and Corollary 4 The bound in theorem 2 does not converge to zero for large N .


We construct a Markov chain with three states a 0 = 0, a 1 = c 0 • √ d and a 2 = c 1 • √ d governed by the following transition distribution: Let X t be the state at time t. The principal eigenvector of the transition matrix ( 1 3 , 1 3 , 1 3 ) gives the stationary probability distribution of X t .

x t ∈ {a 1 , a 2 } with probability 2 3 .


Grammatical functions are also distributed according to a prominence scale.

we find that most subjects are Agents, most objects are Patients or Themes, and most indirect objects are Goals, for example.


The largest drops occur for the structural (δ = −11) and insertion/deletion (δ = −8) features.

all feature groups appear to contribute to the good correlation of RTER.


In the case of Metric B, the high scored translations can achieve both low or high quality according to human judges but low scores ensure low quality.

the same Pearson coefficient may hide very different behaviours.


We have seen that correlation with human judgements has serious limitations for metric evaluation.

we have focused on other aspects of metric reliability that have revealed differences between n-gram and linguistic based metrics: 1.


In most of these cases, synonymy can only be identified from the discourse context.

terminological resources (e.g., WordNet) are not enough to tackle this problem.


Then, the upper bound on the quality of the combined system is given by the   We use the sum of adequacy and fluency, both in a 1-5 scale, as a global quality measure.


In the absence of linguistic knowledge, the system models linguistic structure using an SCFG that contains only one type of nonterminal symbol 1 .

the system is susceptible to the overgeneration problem: the grammar may suggest more reordering choices than appropriate, and many of those choices lead to ungrammatical translations.


Following this work, the existence of many efficient TSP algorithms then inspired certain adaptations of the underlying techniques to SMT decoding for word-based models.

(Germann et al., 2001) adapt a TSP subtour elimination strategy to an IBM-4 model, using generic Integer Programming techniques.


A crucial fact about dependency grammars is that words have preferences about the number and arrangement of arguments and modifiers they accept.

it is desirable to include features that indicate, for a candidate arborescence, how many outgoing arcs depart from each vertex; denote these quantities by v i a∈δ + (i) z a , for each i ∈ V .


A hallmark of many of these models is that they can be implemented very efficiently.

transition-based parsers normally run in linear or quadratic time, using greedy deterministic search or fixed-width beam search Attardi, 2006;Johansson and Nugues, 2007;Titov and Henderson, 2007), and graph-based models support exact inference in at most cubic time, which is efficient enough to make global discriminative training practically feasible (McDonald et al., 2005a;McDonald et al., 2005b).


While such supervised approaches have yielded accurate parsers (Charniak, 2001), the syntactic annotation of corpora such as the Penn Treebank is extremely costly, and consequently there are few treebanks of comparable size.

there has been recent interest in unsupervised parsing.


The second term (the rest of the equation) is the predicted covariance between the constraint function G and the model feature function F j .

if the constraint is not satisfied, GE updates parameters for features that the model predicts are related to the constraint function.


The oracle feature selection method aims to choose such constraints that help to reduce uncertainty in the possible parents constraint set.

we consider sequence features g s with p(edge|g s = 1) ≥ 0.75, and whose corresponding (parent-CPOS,child-CPOS,direction,distance) constraint g, has edge probabilityp(edge|g) ≤ 0.25.


Being lack of non-statements in the training data could cause problems, when the testing data contain many interrogative or imperative sentences as in the BROWN corpus.

the unbalanced distribution of linguistic phenomena in the training data leads to inadequate parser output structures.


As mentioned before, MSTParser is a graphbased statistical dependency parser, whose learning procedure can be viewed as the assignment of different weights to all kinds of dependency arcs.

the feature model focuses on each kind of head-child pair in the dependency tree, and mainly contains four categories of features (Mcdonald et al., 2005a): basic uni-gram features, basic bi-gram features, in-between POS features, and surrounding POS features.


It is emphasized by the authors that the last two categories contribute a large improvement to the performance and bring the parser to the state-of-the-art accuracy.

we extend this feature set by adding four more feature categories, which are similar to the original ones, but the dependency relation was replaced by the dependency backbone of the HPSG outputs.


The basic data structures are a stack, where the constructed dependency graph is stored, and an input queue, where the unprocessed data are put.

the feature model focuses on the tokens close to the top of the stack and also the head of the queue.


For example, the organization name "北京诺基亚通 信 有 限 公 司 (Beijing Nokia Communication Ltd.)" contains a company name (诺基亚/Nokia) and a location name (北京/Beijing).

the translation of organization names should combine transliteration and translation together.


The profile based CDC method generates a partition of E, represented by a partition matrix U (where u ij denotes the membership of an entity e j to the ith identity cluster).

the chained entities placed in a name cluster are deemed as coreferent.


Note that KXBI is only defined for C > 1.

we pick the C that corresponds to the first minimum of KXBI, and then compare its objective function value J C with the cluster variance (J 1 for C = 1).


In comparing the results from one annotator to the results from both annotators, we found substantial agreement.

we present results from the single annotator so we can do a more in-depth analysis.


Also, bilingual instance dictionary D BI is defined as the translation pairs of instances in X S and X T .

in the case of hyponymy-relation acquisition in English and Japanese, (s, t) ∈ D Bi could be (s=(enzyme, hydrolase), t=(Þ (meaning enzyme), AÄ$F Þ (meaning hydrolase))).


When writing a concept definition, one aims to formulate a concise text that includes the most characteristic aspects of the defined concept.

a definition is a promising source for LR relations between the defined concept and the definition terms.


Since a concept definition usually employs more general terms than the defined concept (Ide and Jean, 1993), the concept title is more likely to refer to terms in its definition rather than vice versa.

the title is taken as the LHS of the constructed rule while the extracted definition term is taken as its RHS.


Typical IR datasets, on the other hand, are partially annotated through a pooling procedure.

some of our valid lexical expansions might retrieve non-annotated documents that were missed by the previously pooled systems.


Once is determined, we are able to compute ( | , ), the probability of item a belonging to semantic class z.

we can determine the members of a semantic class z by selecting those items with high , values.


For most items q, this is a larger value than the real number of semantic classes the item belongs to.

one real semantic class may be divided into multiple topics.


As a result, one real semantic class may be divided into multiple topics.

one core operation in this phase is to merge those topics into one semantic class.


In addition, the items in each semantic class need to be properly ordered.

main operations include, 1) Merge semantic classes 2) Sort the items in each semantic class Now we illustrate how to perform the operations.


The reason may be that most low-frequency items are noisy ones.

preprocessing can improve both results quality and processing efficiency; and h=4 seems a good choice in preprocessing for our dataset.


G 0 assumes the empty source sentence consists only of a single wall node.

every word generated under G 0 aligns to null, and we can simplify the dynamic programming algorithm that scores a tree τ s under G 0 : where the final product is 1 when t i has no children.


The weight goes up sharply when it is used in the sample and then is pulled back toward zero gradually by the L1 penalty.

the weight fails to become zero if the feature is used toward the end of training, which is the case in this example.


To make matters worse, language is Zipf-distributed, so that a large fraction of any training data set will be hapax legomena, very many word types will appear only a few times, and many word types will be left out of the training set altogether.

for many word types supervised NLP systems have very few, or even zero, labeled examples from which to estimate parameters.


For supervised sequence-labeling problems in NLP, the most important "complicating factor" that we seek to avoid through smoothing is the data sparsity associated with word-based representations.

the task is to find g such that for every word x, g(x) is much less sparse, but still retains the essential features of x that are useful for predicting its label.


The HMM finds (probabilistic) categories Y for both the current word x i and the context word x i−1 , and models the probability that one category follows the other: P (Y i |Y i−1 ).

the HMM is our most extreme smoothing model, as it aggregates information over the greatest number of examples: for a given consecutive pair of words x i−1 , x i in the test set, it aggregates over all pairs of consecutive words x i−1 , x i where x i−1 is similar to x i−1 and x i is similar to x i .


Unfortunately, Zhang and Clark's experimental setting did not allow us to use our errordriven policy since performing 10-fold cross validation again on each main cross validation trial is computationally too expensive.

we used our baseline policy in this setting and fixed r = 3 for all cross validation runs.


In the work, the highest priority concept on captioning is that one screen should be filled with as much text as possible.

a semantic boundary in a sentence is hardly taken into account in linefeed insertion, and the readability of the caption is hardly improved.


It is thought that a pause corresponds to a syntactic boundary.

there are possibility that a linefeed becomes more easily inserted into a bunsetsu boundary at which a pause exists.


The baseline 1 is very simple method which inserts linefeeds into the bunsetsu boundaries so that the length of the line does not exceed the maximum number of characters per line.

the recall and precision were the lowest.


The purpose of our research is to improve the readability of the spoken monologue text by our linefeed insertion.

we conducted a subjective evaluation of the texts which were generated by the above-mentioned experiment.


When multiple people insert linefeeds for the same text, there is possibility that linefeeds are inserted into different points.

for one lecture data (128 sentences, 511 bunsetsus) in the experimental data, we conducted an experiment on linefeed insertion by an annotator who was not involved in the construction of the correct data.


However, compared with baseline 1, our method uses a lot of linguistic information which should be provided not by human but by natural language analyzers under the real situation.

to fairly evaluate our method and four baselines, we conducted an experiment on linefeed insertion by using the automatically provided information on clause boundaries and dependency structures 5 .


However, it is very expensive and time-consuming to perform prosody labeling manually.

automatic labeling of prosodic events is an attractive alternative that has received attention over the past decades.


Most of the previous work for prosodic event detection reported their results using classification accuracy instead of F-measure.

to better compare with previous work, we present below the accuracy results in our approach.


The premise of this approach, however, is to reliably find reoccuring acoustic patterns in audio, which is challenging because of noise and pronunciation variance existing in the speech signal, as well as the difficulty of finding alignments with proper lengths corresponding to words well.

our primary goal in this paper is to empirically determine the extent to which acoustic information alone can effectively replace conventional speech recognition with or without simple prosodic feature detection within the multi-document speech summarization task.


In addition, some nodes will often have the same labels and spans.

it is more convenient to use an identity for referring to a node.


On our Chinese-English corpus, we find that 38% of minimal frontier tree pairs are not pairs of minimal frontier trees.

we have to first collect all frontier tree pairs and then decide on the minimal ones.


When p = 10 and n = 10, the coverage was 9.7%, higher than that of p = 0.

packed forests enable tree-to-tree models to capture more useful source-target mappings and therefore improve translation quality.


Lines 7-9 of the former and line 7 of the latter are equivalent for linear S(e; e ).

for any linear similarity measure, Algorithm 3 is an algorithm for minimum Bayes risk decoding.


On the contrary, a string-to-tree decoder (e.g., (Galley et al., 2006;Shen et al., 2008)) is a parser that applies string-to-tree rules to obtain a target parse for the source string.

the hypothesis structures of the two models are fundamentally different.


Despite the diversity of translation models, they all have to produce partial translations for substrings of input sentences.

we represent the search space of a translation model as a structure called translation hypergraph.


The shaded nodes are shared by both models.

the two models are combined at the translation level.


Suppose that the left bound of x is 0, we compute the function values for t 1 , t 2 , and t 3 at x = 0 and find that t 3 has the greatest value.

we choose x = 0 as the first critical intersection.


For 15,157 sentences we failed to obtain 1-best parses.

only 93.7% of the training corpus were used by the tree-to-string model.


We write D(x, y) def = {d ∈ D(x) : Y(d) = y} to denote the set of all derivations that yield y.

the set of translations permitted by the model is .


To obtain p(y | x) above, we need to marginalize over a nuisance variable, the derivation of y.

the MAP decision rule becomes where is typically derived from a loglinear model as follows, where γ is a scaling factor to adjust the sharpness of the distribution, the score s(x, y, d) is a learned linear combination of features of the triple (x, y, d), and Z(x) is a normalization constant.


So the most probable string y * under q * may be simply "the mouse," which is short and has a high probability but fails to cover x.

a better way of using q * is to restrict the search space to the original hypergraph, i.e.


In contrast, (Y search) scores well with the 'police' chain and (search X) scores well in the 'defendant' chain too.

we want to favor search instead of pull over because the schema is already modeling both arguments.


Of the 17 misaligned events, we judged all but one to be correct in a narrative sense.

although not exactly aligned with FrameNet's notion of event clusters, our induction algorithm seems to do very well.


In an MRLG, each production rule introduces a single predicate in the MRL, where the type of the predicate is given in the left hand side (LHS), and the number and types of its arguments are defined by the nonterminals in the right hand side (RHS).

the parse of an MR also gives its predicate-argument structure.


Possible latent concepts for this set include the concepts along all paths from its attachment point to the WN root, following Is-A relation edges.

any two labeled attribute sets share a number of latent concepts based on their similarity in WN: all labeled attribute sets share at least the root concept, and may share more concepts depending on their most specific, common ancestor.


When we establish a system of aspects, we say that the features used are necessary and sufficient if different aspects included can be distinguished from each other by means of these features.

the more aspect expressions are involved, the more aspectual features are needed.


4 Using vector-based models of semantic representation to account for the systematic variances in neural activity Computational linguists have demonstrated that a word's meaning is captured to some extent by the distribution of words and phrases with which it commonly co-occurs (Church and Hanks, 1990).

encoded the meaning of a word as a vector of intermediate semantic features computed from the co-occurrences with stimulus words within the Google trillion-token text corpus that captures the typical use of words in English text.


Notice how the additive model concatenates the meaning of two words and is heavily loaded on see, eat, and smell, whereas the multiplicative model zeros out unshared features like eat and smell.

the multiplicative model predicts that the visual aspects will be emphasized when a participant is thinking about strong dog, while the additive model predicts that, in addition, the behavioral aspects (e.g., eat, smell, and hear) of dog will be emphasized.


Therefore, meaning associated with the noun should be more evoked.

we predicted that the noun model would outperform the adjective model.


The basic proposition of the model is that the human semantic knowledge system is capable of storing an infinite list of features (or semantic components) associated with a concept; however, only a subset is actively recalled during any given task (contextdependent).

a set of latent indicator variables is introduced to indicate whether a feature is actively recalled at any given task.


However, such approaches tend to disregard theoretical findings from discourse theories, such as Centering Theory (Grosz et al., 1995).

one of the challenging issues in this area is to incorporate such findings from linguistic theories into machine learning-based approaches.


Syntactic patterns are useful clues for intra-sentential zero-anaphora resolution, whereas rhetorical clues such as connectives may be more useful for inter-sentential cases.

the intra-sentential and intersentential zero-anaphora resolution models are separately trained by exploiting different feature sets as shown in Table 2.


However, if the zero-anaphora resolution in the current utterance is performed correctly, it will be available for use as information about the recency of candidates and the anaphoric chain of each candidate.

we also investigate whether correct zero-anaphora resolution contributes to the dynamic cache model or not.


person vs. location) are quite accurate.

we would expect a coreference resolution system to depend critically on its Named Entity (NE) extractor.


As pointed out by Reitter (Reitter, 2003a), we can hypothesize a correlation between span length and some relations (for example, the satellite in a CONTRAST relation will tend to be shorter than the nucleus).

it seems useful to encode different measures of span size and positioning, using either tokens or edus as a distance unit: Ex.


Other complicated FS methods are combinations of these two measurements.

we regard the two measurements as basic, which are referred to as the frequency measurement and ratio measurement.


It is reported that this method is biased towards low frequency terms and the bias becomes extreme when ( ) P t is near zero.

it can be seen in the following formula (Yang and Pedersen, 1997)  this method might perform badly when common terms are informative for classification.


Since the methods of DF and MI only utilize the document frequency and category information respectively, we use the DF scores and MI scores to represent the information of the two basic measurements.

we would select the top-2% terms with each method and then investigate the distribution of their DF and MI scores.


We hypothesize that a high accuracy can be achieved only if the classifier is trained on both ambiguous and unambiguous reviews.

we apply active learning (Cohn et al., 1994) to identify the ambiguous reviews.


Sentence Semantic Component Analysis: Using shallow semantics, we decode the underlying dependency trees that embody linguistic relationships such as head-subject (H-S), head-modifier Sentence Structure Analysis: In our question analysis, we observed that 98% of affirmed questions did not contain any object and they are also in copula (linking) sentence form that is, they are only formed by subject and information about the subject as: {subject + linking-verb + subjectinfo.}.

we investigate such affirmed questions different than the rest and call them copula sentences and the rest as non-copula sentences.


-Verb match statistics using WordNet's cause and entailment relations.

each q/a pair is represented as a feature vector x i ∈ d characterizing the entailment information between them.


Since copula and non-copula sentences have different structures, e.g., copula sentences does not usually have objects, we used different sets of features for each type.

we modify edge weights in (1) as follows: (2) The diagonal degree matrix D is defined for graph g by D= jw ij .


As the weight matrix gets denser, meaning there will be more data points with connected weighted edges, the more it takes to learn the classifier function via graph.

the question is, how can one reduce the data points so that weight matrix is sparse, and it takes less time to learn?


We use the normalized density constraints for ease of calculations.

for a each sample summary dataset, a local density constraint vector is identified as δ s = {δ s 1 , ..., δ s nb } T .


Algorithm 1 Graph Summary of Large Dataset 1: Given X = {x1, ..., xn} , X = XL ∪ XU 2: Set q ← max number of subsets 3: for s ← 1, ..., q do 4: Choose a random subset with repetitions 5: After all data points are evaluated, the sample dataset X s can now be represented with the summary representative vertices as and corresponding local density constraints as, The summarization algorithm is repeated for each random subset X s , s = 1, ..., q of very large dataset X = X L ∪ X U , see Algorithm 1.

q number of summary datasets X s each of which with nb labeled data points are combined to form a representative sample of X, X = X s q s=1 reducing the number of data from n to a much smaller number of data, p = q * nb n. So the new summary of the X can be represented with X = X i p i=1 .


We thus consider two path simplifications as well: • compressed: only the source, target, and root nodes are preserved in the path (so the path above becomes [VBP ↑ S ↓ NP]) • POS class clusters: rather than distinguish, for example, between different tenses of verbs in a path, we consider only the first letter of each NT.

clustering compressed output, the new path from predicate to ARG0 becomes The top paths were similarly consistent regardless of whether paths are extracted from P ra , P vm , or P va (P va results shown in Table 4), but we see that the distributions of paths are much flatter (i.e.


It can be assumed that a one-hour lecture given by the same instructor will exhibit a strong cohesion, both in topic and in speaking style, between its parts.

in contrast to typical TBL solutions, we have evaluated our TBL-based approach by partitioning each 50 minute lecture into a training and a test set, where the training set is smaller than the test set.


hours 4 for a threshold of 2 when training over transcripts for one third of a lecture.

it can be concluded that, despite being computationally more intensive than a heuristic approximation (for which the learning time is on the order of just a few minutes), a TBL system using a global, WERcorrelated scoring function not only produces better transcripts, but also produces them in a feasible amount of time with only a small amount of manual transcription for each lecture.


Moreover, the inference procedure for each sentence pair is non-trivial, proving NP-complete for learning phrase based models  or a high order polynomial (O(|f | 3 |e| 3 )) 1 for a sub-class of weighted synchronous context free grammars (Wu, 1997).

for such models both the parameterisation and approximate inference techniques are fundamental to their success.


Phrasal SCFG models are subject to a degenerate maximum likelihood solution in which all probability mass is placed on long, or whole sentence, phrase translations (DeNero et al., 2006).

careful consideration must be given when specifying the P 1 distribution on terminals in order to counter this behavior.


Unlike the Gibbs updates, this calculation cannot be distributed over a cluster (see Section 4.4) and thus is very costly.

for small corpora we re-sample the hyperparameter after every pass through the corpus, for larger experiments we only re-sample every 20 passes.


Essentially, all morphological forms of a word and its translations have to exist in the training corpus, and every word has to appear with every possible case marker, which will require an impossible amount of training data.

it is imperative to make it possible for the system to learn general rules for morphology and case marking.


For example in Chinese, the object almost always occurs behind its dominating verb; the attribute modifier always occurs in front of its head word.

we can draw a conclusion that the relative positions between head and dependent of VOB and ATT can be determined by the types of dependency relations.


In subtree c of Figure 2, the dependency relation be-tween the only dependent and the head is ATT, which indicates that the dependent is a predependent.

node 7 is bound to precede node 5, and the only linearization result is "武汉 航空".


'Chunk 7' in the compressed sentence was constructed by dropping both content and functional words and joining other content words contained in 'Chunk 4' and 'Chunk 6' of 2 Generally, a dependency relation is defined between bunsetsu.

in order to identify word dependencies, we followed Kudo's rule (Kudo and Matsumoto, 2004) the original sentence.


PLM is similar to dependency probability in that both features emphasize word pairs that occurred as bigrams in the source sentence.

by introducing dependency probability, the information within the feature vector is not increased even though the number of features is increased.


In SMT, words of an input sentence should be totally translated, whereas in SPG, not all words of an input sentence need to be paraphrased.

a SPG model should be able to decide which part of a sentence needs to be paraphrased.


Lin and Pantel (2001) have demonstrated that the overlapping between the automatically acquired paraphrases and handcrafted ones is very small.

the human references cannot properly assess the quality of the generated paraphrases.


• Sentence simplification: Sentence simplification requires using common expressions in sentences so that readers can easily understand the meaning.

only the target units more frequent than the sources are kept in paraphrase planning.


Our method is not restricted in domain or sentence style.

any sentence can be used in development and test.


However, for the sentence similarity computation purpose in our experiments, we want to evaluate if the method can enhance the stringlevel similarity between two paraphrase sentences.

for each input sentence s, we need a reference sentence s for similarity computation.


The difference from our method is that Baseline-2 does not take different applications into consideration.

it contains no paraphrase planning stage or the usability sub-model.


This not only suggests the difficulty of the cause identification task, but also reveals the vagueness inherent in the definition of the 14 shapers.

we had the two annotators re-examine each report for which there was a disagreement and reach an agreement on its final set of labels.


However, an exact solution to this optimization problem is computationally expensive.

we find a local maximum by employing a local search algorithm, which alters one parameter at a time to optimize F-measure by holding the remaining parameters fixed.


Higher the LCSRatio and lower the EditDistance SM S , higher will be the similarity measure.

for example, for a given SMS token "byk", similarity measure of word "bike" is higher than that of "break".


The rationale for this stems from the fact that queries, in general, are composed of informative words.

for example, for a given SMS token "byk", idf of "bike" will be more than that of commonly occurring word "back".


Thus for example, for a given SMS token "byk", idf of "bike" will be more than that of commonly occurring word "back".

even though the similarity measure of "bike" and "back" are same w.r.t.


Preparing labeled data, however, is very expensive.

in cases where there is no or a small amount of labeled data available, these models do a poor job.


For the notation convenience and consistancy, throughout this paper, we show terminals and non-terminals by lowercase and uppercase letters, respectively and sets and multisets by bold font uppercase letters.

using the rules in (6) a sentence of the language (which is a multiset in this model) can be derived as follows: Once the set is generated, it can be realized as any of the six permutation of d, e, and c. a single sequence of derivations can lead to six different strings of words.


The probabilistic parser keeps a probability p for every active arc and every constituent: When extending γ using c, we have: When creating c from the completed active arc γ : Although search queries are usually short, the running time is still an issue when the length of the query exceeds 7 or 8.

a couple of techniques have been used to make the naïve algorithm more efficient.


Furthermore, the definition of the tree structure for a query is quite arbitrary.

even when human resources are available, building such a Treebank is not a trivial task.


The resources available for training and testing were a set of search queries from the product domain.

a set of CFSG rules were written for the product domain.


Figure (7a) shows an example of a parse tree generated for the query "Canon vs Sony Camera" in which B, Q, and T are abbreviations for Brand, Query, and Type, and U is a special tag for the words that does not fall into any other tag categories and have been left unlabeled in our corpus such as a, the, for, etc.

the parser assigns the tag sequence B U B T to this query.


For example, in Table 1, when deciding the student's answer after the second tutor question, the simulation looks back into the dialog and finds out that the last time (in Student1) the student answered a question from the same cluster 3rdLaw incorrectly.

this time the simulation gives a correct student answer based on the probability P (c|3rdLaw, ic).


This time, the correct simulated user action is ranked second.

the reciprocal ranking of this simulation action is 1/2.


In addition, based on the one-sense-per-discourse assumption, the recognition of abbreviation definitions assumes senses of abbreviations that are locally defined in a document.

a number of studies have attempted to model the generation processes of abbreviations: e.g., inferring the abbreviating mechanism of the hidden markov model into HMM.


The implicit (DPLVM) and explicit (GI) solutions address the same issue concerning the incorporation of non-local information, and there are advantages to combining these two solutions.

we will combine the implicit and explicit solutions by employing the GI encoding in the DPLVM (DPLVM+GI).


Since the objective function of the DPLVM model is non-convex, different parameter initializations normally bring different optimization results.

to approach closer to the global optimal point, it is recommended to perform multiple experiments on DPLVMs with random initialization and then select a good start point.


All of its top-1, top-2, and top-3 accuracies were consistently better than those of the CRF model.

this demonstrated the effectiveness of using the latent variables in Chinese abbreviation generation.


However, the source side of r 1 does not match the source tree structure of the test sentence.

we can only partially translate the illustrated test sentence with this training sample.


The gaps for the insertion of the tree sequences in the target side are generated from either the inherit- generate translations by replacing the nonterminal leaf nodes of r with their corresponding spans' translation 6: insert the new translation into h[j 1 , j 2 ] 7: end if 8: end do ance of the target side non-contiguous tree sequence pairs or the production of the previous operations of "Source gap insertion".

the insertion for target gaps helps search for a better order of the non-contiguous constituents in the target side.


Consequently, this distortional operation, like phrase-based models, is much more flexible in the order of the target constituents than the traditional syntax-based models which are limited by the syntactic structure.

"Target tree sequence reordering" enhances the reordering ability of the model.


The set of such ITG alignments, A IT G , are a strict subset of A 1-1 (Wu, 1997).

we will view ITG as a constraint on A 1-1 which we will argue is generally beneficial.


This relatively small improvement is mainly due to the selection of the whole sentence alignment: for many sentences the best alignment still contains alignment errors, some of which could be fixed by other aligners.

it is desirable to combine alignment links from different alignments.


For CLA word alignment, if we use the same association score, direct and inverse CLA word alignments should be exactly the same.

we use different functions to compute the surface similarities, such as using maximum common subsequence (MCS) to compute inverse word alignment, and using longest matched prefix (LMP) for computing direct word alignment, as in Equation 4  2) When two word alignments are ready, we start from the intersection of the two word alignments, and then continuously add new links between backbone and hypothesis if and only if both of the two words of the new link are unaligned and this link exists in the union of two word alignments.


The reason could be that the similarity functions used in the two directions are more similar.

there are not so many inconsistent links between the two directions.


Effect of fuzzy matching in TER: the previous work on TER-based word alignment uses hard match in counting edits distance.

it is not able to handle cognate words match, such as in Figure 2, original TER script count the edit cost of (shoot, shot) equals to word pair (shot, the).


If hypothesis 2 is aligned before hypothesis 1, the final CN will not be good.


In incremental IHMM, suppose the i-th span covers two arcs e a and , with probabilities p 1 and p 2 = 1 − p 1 respectively, then the transition from state i to i + 1 jumps across one word (e a ) with probability p 1 and jumps across nothing with probability p 2 .

the transition probability should be Suppose further that the (i + 1)-th span covers two arcs e b and , with probabilities p 3 and p 4 respectively, then the transition from state i to i + 2 covers 4 possible cases: Accordingly the transition probability should be The estimation of transition probability can be generalized to any transition from i to i by expanding all possible n-grams covered by the transition and calculating the corresponding probabilities.


Therefore, The second option is due to the constraint that a null state is accessible only to itself or the real word state associated with it.

the transition from i to i is in fact composed of the first transition from i to δ(i, k) and the second transition from δ(i, k) to the null state at i.


The only real difference between the algorithms in this limited case is that EXH places k-best items on local priority queues per edge, while KA * makes use of one global queue.

in addition to providing a method for speeding up k-best extraction with A * , our algorithm also provides an alternate form of Huang and Chiang (2005)'s k-best extraction that can be phrased in a weighted deduction system.


We calculate a bound for the fan-out of X \D 1 as follows: we observe that all the endpoints in E(X \ D 1 ) must be either endpoints of X or endpoints of But we also observe that E(X \ D 1 ) cannot contain any of the endpoints where D 1 is adjacent to D 2 (i.e., the members of E(D 1 ) ∩ E(D 2 )), since X \ D 1 does not contain D 1 or D 2 .

we can say that any endpoint of X \ D 1 is either a mem- the number of endpoints in E(X \ D 1 ) cannot exceed the sum of the number of endpoints in these two sets, which, according to the reasonings above, is at most 4 − f (D 1 ) + f (D 1 ) = 4.


Furthermore, even though TT-MCTAG arguments can adjoin with a delay to their head, their possible adjunction site is restricted with respect to their head.

one obtains a slight degree of locality that can be exploited for natural language phenomena that are unbounded only in a limited domain.


In other words, we want to check whether a TAG derivation tree is a valid TT-MCTAG derivation tree without deciding, for every occurrence of some argument β, which of the h(β)-nodes represents its head.


Node v has a single child represented by the root of D ′ .

the t-counter associated with D should be T ′ + T (q,r) .


The number of t-counters satisfying ||T || m ≤ n is O(n c G ), with c G = Since all of the other components in an item are bounded by O(n 4 ), there are polynomially (in n) many items that can be constructed for an input w. It is not difficult to see that each individual item can be constructed by a number of rule applications bounded by a polynomial as well.

the total number of applications of our deduction rules is also bounded by some polynomial in n. We thus conclude that the languages generated by the class TT-MCTAG are all included in PTIME.


Since there may be many sentences containing a given entity pair, we can extract very large numbers of (potentially noisy) features that are combined in a logistic regression classifier.

whereas the supervised training paradigm uses a small labeled corpus of only 17,000 relation instances as training data, our algorithm can use much larger amounts of data: more text, more relations, and more instances.


We generate a conjunctive feature for each k ∈ {0, 1, 2}.

each lexical row in Table 3 represents a single lexical feature.


We generate one conjunctive feature for each pair of left and right window nodes, as well as features which omit one or both of them.

each syntactic row in Table 3 represents a single syntactic feature.


When S is large, the problem becomes more like traditional supervised learning, and our setting of λ T µ = 10 4 is no longer optimal because we are now not afraid of overfitting the large set of seed target instances.

we also included another TL-NE experiment with λ T µ set to 10 2 .


noun relations "CEO", "founder" between a person and a company.

for each concept pair, a list of relational terms is collected.


If, on the other hand, class boundaries are already too well defined due to an overly large seed set, nothing to be learned is left.

together with low thresholds, a delay rate of d > 0 might be crucial to obtain models of high performance.


Self-training and co-training, in contradistinction, select examples of high confidence only.

these bootstrapping methods will presumably not find the most useful unlabeled examples but require a human to review data points of limited training utility (Pierce and Cardie, 2001).


feature vector) X generated from a given phrase and its constituents, whereas the output is the probability predicted by fitting X to a logistic curve.

λ is replaced as follows: where α is a scaling factor to confine the output to values between 0 and α. where x i is the ith feature, β i is the coefficient parameter of x i , and β 0 is the 'intercept', which is the value of f (X) when all feature values are zero.


Second, we present our method to automatically generate a data set, and evaluate the effectiveness of this technique.

of this evaluation, we obtain a human-annotated data set which we use to evaluate the systems implemented in the following sections.


3 In our case, this task gets already accomplished by the crawler for all the web pages crawled by the search engine.

we can summarize our information in two separate tables; T 1 and T 2.


Thus we eliminate the queries that have a total click frequency above a given frequency threshold F , and, that have less than a given distinct number of urls, U .

we have three parameters that help us in eliminating the noise from the inital data; W , F , and U .


Commonly adopted metrics for ranking, such as mean average precision (Buckley and Voorhees, 2000) and Normalized Discounted Cumulative Gain (Järvelin and Kekäläinen, 2000), is designed for data sets with human relevance judgment, which is not available to us.

we use the Kendall's tau coefficient (Kendall, 1938;Joachims, 2002) to measure the degree of correlation between two rankings.


For simplicity, let's assume strict orderings of any given ranking.

we ignore all the pairs with ties (instances with the identical click count).


However, Outside arcs also appear in partial paths between two coordinations, as illustrated in Figure 2.

two differ- We identify this to be a cause of their model not performing well for Japanese, and propose to augment the original alignment graph with a "bypass" devoted to explicitly indicate that no coordination exists in a sentence; i.e., we add a special path directly connecting the initial node and the terminal node of an alignment graph.


From the research point of view, it suggests more should be considered in addition to grapheme mapping for handling Cantonese data.

we have compared E2C between Mandarin Chinese and Cantonese, and discussed work in progress for our proposed SoTo method which more reasonably treats homophones and better models tonal patterns in transliteration.


The most important contribution of this work is that it produces an output that is better than the user input even if it contains few spacing errors.

the proposed method can be applied as a pre-processing module for practical NLP applications without introducing a risk that would generate a worse output than the user input.


The morpheme-based representation above cannot explicitly state the boundaries of bunsetsus.

we add the type to every dependency relation.


His parser, which cannot do bunsetsu chunking, accepts only a chunked sentence and then produces a bunsetsu-based dependency structure.

we cannot directly compare results with ours.


A collocation is defined as a pair of nouns cooccuring within a snippet.

each 1 for "red carpet", corpora will be collected for "red carpet" and "carpet".


To the best of our knowledge there are no noun compound datasets accompanied with compositionality judgements available.

we developed an algorithm to aid human annotation.


Our research goal was to develop a directional similarity measure suitable for learning asymmetric relations, focusing empirically on lexical expansion.

we aimed to quantify most effectively the above notion of feature inclusion.


Selectional preferences introduce semantic generalizations on the type of arguments preferred by the predicates.

they are expected to improve generalization on infrequent and unknown words, and increase the discriminative power of the argument classifiers.


Second Language Acquisition (SLA) researchers have claimed that feedback provided during conversational interaction facilitates the acquisition process.

interest in developing Dialog-Based Computer Assisted Language Learning (DB-CALL) systems is rapidly increasing.


tourist information, legal/social advice) non-native speakers represent a significant portion of the everyday user population.

(Raux and Eskenazi, 2004) conducted a study on adaptation of spoken dialog systems to non-native users.


In particular, DB-CALL systems should obviously deal with grammar errors because language learners naturally commit numerous grammar errors.

grammar error simulation should be embedded in the user simulation for the development and evaluation of such systems.


However, Korean/Japanese language learners of English tend to make subjectverb agreement errors, omission errors of the preposition of prepositional verbs, and omission errors of articles because their first language does not have similar grammar rules so that they may be slow on the uptake of such constructs.

they often commit errors like the second simulated output.


If an argument of a predicate is a zero pronoun, it is likely that the argument itself has appeared in previous sentences.

the analyzer collects not only all phrases in the sentence but also some phrases in the previous sentences.


Even if syntactic information does not help to identify arguments, we can expect that a certain noun phrase might be the correct argument of the predicate when we put it in place of the zero pronoun and the sentence becomes meaningful.

we add language model scores as features of the identifier.


However, the Used features are affected by the accuracy of the previous analyses.

we also adopt the Salience Reference List (Nariyama, 2002), which only uses explicit surface case markers or a topic marker, and added  Another way to adopt contextual information is to restrict the candidate arguments.


We further assume that the remaining utterances are incorrect because users do not often respond with "no" for explicit confirmations containing incorrect content and instead repeat their original utterances.

we regard that the ASR results of the following utterances are correct: (1) affirmative responses and their immediately preceding utterances and (2) negative responses.


The prediction accuracy was 72.7% when only the "correct" ASR accuracy was used, and the prediction accuracy was 71.8% when only the barge-in rate was used.

the prediction accuracy was better when both inputs were used rather than when either input was used.


We augmented the feature set to include a class of features about structural information that tells us when the subject (or object) of a verb can be dropped while the verb itself is retained.

now if the system does retain the are, it is more likely to retain the correct arguments of the word from the original sentence.


As a matter of fact, the concepts in the original document set are not all necessary to be included in the summary.

before constructing the hierarchical representation, we first conduct a filtering process to remove the unnecessary concepts in the document set in order to improve the accuracy of the hierarchical representation.


However, a single PMIbased measure is not enough to characterize the word relation.

the constructed tree can not always well represent the concepts for some document sets.


The top K ranked sentences by f 2 are supposed to be highly supported by f 2 .

a sentence that is similar to those top ranked sentences should deserve a high rank as well.


the phrase length, the frequency that a sub-phrase appears in longer phrases.

it is appropriate for extracting hierarchical phrases.


Given the threshold ε = 5, f 1 and f 3 are viewed as key phrases.

r 2 will be discarded because its source-side is not a key phrase.


Long sentences could be very complicated in grammar and sentence structure, thereby creating an obstacle for translation.

we need to break them into shorter and easier clauses.


However, simply applying this method might damage the accuracy of parsing.

the strategy we proposed is to operate division while concerning the structure of parse tree.


As can be seen in Figure 2, when we divide the sentence by comma, it would break the structure of "VP" sub-tree and result in a ill-formed sub-tree "VP" (right sub-tree), which don't have a subject and don't respect to original tree structures.

the key issue of sentence division is finding the sentences that can be divided without loosing parsing accuracy.


Till we have checked all the LCA of each word and comma, we finally find that all the LCA are "IP".

this sentence can be divided without loosing parsing accuracy.


In dependency trees, the independence assumptions made by HMTM correspond to the intuition of linguistic dependency.

we suggest to use HMTM and tree-modified Viterbi algorithm for tasks interpretable as labeling nodes of dependency trees.


Finally, we use machine learning techniques to eliminate non-comparative sentences from the candidates.

we achieved significant performance, an F1-score of 88.54%, in our experiments using various web documents.


([Nae sang-gak-en nae-il bi-ga ol geot gat-a-yo]: I think it will rain tomorrow.)

all the sentences can be divided into four categories as follows: Our final goal is to find an effective method to extract S1 and S2, but single-keyword searching just outputs S1 and S3.


Secondly, many sources are present in blogs, as people introduce quotes from newspaper articles or other information to support their arguments and make references to previous posts in the discussion thread.

when performing a task such as Question Answering (QA), many new aspects have to be taken into consideration.


We believe the reason is that question 18 was ambiguous as far as polarity of the opinions expressed in the answer snippets ("improvement" does not translate to either "positive" or "negative") and question 20 referred to the title of a project proposal that was not annotated by any of the tools used.

as part of the future work in our OQA system, we must add a component for the identification of quotes and titles, as well as explore a wider range of polarity/opinion scales.


This implies that weasel tags are short lived, very sparse and that -because weasels may not have been discovered yet -not all occurrences of linguistic hedges are tagged.

we collected not one but several Wikipedia dumps 3 from the years 2006 to 2008.


As the number of weasel tags is very low considering the number of sentences in the Wikipedia dumps, we still expected there to be a much higher number of potential weasel words which had not yet been tagged leading to false positives.

we also annotated a small sample manually.


from  After the summary is given, for each word in summary, we ask one person to decide whether this word correctly describe the feature.


Also, words describing features usually co-occur with features in the same sentence.

when the features in a sentence are correctly recognized, Words describing those features are likely to be identified by our methods.


Under this circumstance, a prefixed threshold is too restrictive to be applied in complex situations.

a function of n is introduced into the threshold as follows: If k eigenvalues are qualified to be the principal components, then the threshold in Eqn.


Although it is not clear that query logs are the best source of learning semantic categories, all the previous studies using web search logs rely on web search query logs.

we propose to use web search clickthrough logs to learn semantic categories.


Each group corresponds to reports pertaining to a single bug and are duplicate of one another.

reports belonging to the same group can be naturally regarded as parallel sentences.


Instead, they are usually composed of the words with long-distance dependencies, which cannot be easily captured by n-grams.

the aim of this study is two-fold: (1) to automatically discover association language patterns from the sentences annotated with negative life events; and (2) to classify sentences with negative life events using the discovered patterns.


However, it is costly and time consuming to compile travel information for all tourist spots and to keep them up to date manually.

we have studied the automatic compilation of travel information.


Then we manually identified travel blogs in 4,914 entries.

420 entries were identified as travel blogs.


This type of coreference cannot be annotated without a linguistic knowledge and without training.

it must be omitted for the purposes of the PlayCoref game.


The proposed approach requires no prior domain knowledge and no adaptation for new domains.

the method can be used in any domain corpus and it is especially useful for resource-limited domains.


We select the elements that are common in the two definitions (I'=(s→I)∩(t→I)) and we attempt to identify their respective senses from WordNet (sns(I')), comparing each synonym in the WordNet's synonym description with each word from the pivot translations.

we arrive at a certain set of senses from the source-pivot definitions (sns((s→I')) and target-pivot definitions (sns((t→I')).


1 Second, the word order in Korean is relatively free because the functional morphemes, case particles and word endings, play the role as a grammatical information marker.

the functional morphemes, rather than the word order, determine whether a word is a subject or an object.


Technically, the function words they ignored are not 'untranslatable' but are 'unalignable'.

instead of removing the function words, we decide to insert some pseudo words into an English sentence in order to align them with potential Korean function words and make the length of both sentences similar.


Though the word order of a Korean sentence is relatively free, Korean generally observes the SOV word order, and it is a headfinal language.

an object precedes a predicate, and all dependents precede their heads.


In other words, most triple-1 phrase pairs are significant and it is likely that the translation quality will decline if all of them are discarded.

using significance values alone cannot completely resolve the problem of phrase table pruning.


This is because rules are less sparse than phrases in general, as they are extracted with a shorter length limit, and have nonterminals that match any span of words.

the basic pruning scheme works well with rule tables.


(Schütze, 1995;Biemann, 2006)): The most frequent 200 words in the corpus (July 2008 dump of English Wikipedia) are used as features in a word window of ±2 around the target words.

each target word is described by an 800-dimensional feature vector, containing the number of times we observe one of the most frequent 200 words in the respective positions relative to the target word.


(2008) through analysis of the degree distribution of such networks; and (b) a random walk conducted on the semantic DSN will have a high tendency to drift away very soon from the semantic class of the starting node, whereas in the syntactic DSN, the random walk is expected to stay within the same syntactic class for a long time.

it is reasonable to advocate that characterization and processing of syntatic classes is far less confusing than that of the semantic classes -a fact that requires no emphasis.


There is also tendency of many lexical chains ending before a story ends.

there is a higher density of chain starts and ends in the proximity of a story boundary.


We assess whether non-native prosody can be recognized using native or near-native training speech or whether it will be necessary to use matched training data from non-natives of similar skill level or language background.

we perform experiments on matched training and test data, training and testing within groups of speakers.


The productive morphology of Turkish allows one to generate very long words such as ölümsüzleştirdigimizden.

the detection and the correction of spelling errors by presenting the user with a ranked list of spelling suggestions are highly desired.


However, since the sub-trees with -UNF labels in the original corpus are by definition unfinished, the label to the right of the slash (NN in this case) is not defined.

transformed trees with unfinished structure have the representation of Figure 2, which gives away the positive benefits of the right-corner transform in representing repair by propagating a special repair symbol (EDITED) through the grammar.


In practice, unsupported transition features are not parameterized 2 ; this means that λ k = 0 and Ψ 2 j,i = 1 if j ∈ A c i .

this method estimates nearly-exact 1 In practice, dynamically selecting L increases the number of computations, and this is the main disadvantage of Method 1.


This trick for updating P w (y|x) has been used in SCGIS (Goodman, 2002).

the first Newton iteration of all methods discussed here takes O(l) operations.


One benefit of this assumption is that each topic is represented by meaningful sentences, instead of directly by keywords.

we have Here we use parameter U st for the probability of choosing base model s given topic t, p(S i = s|T i = t) = U st , where s U st = 1.


For the third topic (best friend), the contributors were first asked to think about their best friend and describe the reasons for their friendship (including facts and anecdotes considered relevant for their relationship).

in this case, they were asked to tell the truth about how they felt about their best friend.


For instance, non-spammers often use special characters to create emoticons to express their mood, such as ":-)" (smiling) or "T T" (crying), whereas spammers tend to use special character or patterns related to monetary matters, such as "$$$" or "%".

we also measured the ratio of special characters, the number of emoticons, and the number of special character patterns in SMS messages as features.


We assume each individual document sentence as the extract summary and calculate its ROUGE similarity scores with the corresponding abstract summaries.

an average ROUGE score is assigned to each sentence in the document.


For each abstract sentence, we assign a score to every document sentence as the sum of its filtered BE scores divided by the number of BEs in the sentence.

every abstract sentence contributes to the BE score of each document sentence and we select the top N sentences based on average BE scores to have the label +1 and the rest to have the label −1.


Under this assumption, the translation of "被捕" in the above example should be placed in the position between "Saddam" and ".".

once we find a VTG, its translation can be inserted into the corresponding position of the target sentence using the alignment.


Critically, we also know that a missing main verb can cause major problems with comprehension.

readers could better determine if the modified sentence better captured the meaning of the source sentence.


However, we can obtain an exact formula for the expectation by utilising the relationship between the Digamma function and the harmonic numbers: ψ(n) = H n−1 − γ.

4 we can rewrite (5) as: 5 1 The authors of GGJ06 realized this error, and current implementations of their models no longer use these approximations, instead tracking table counts explicitly.


As can be seen in Figure 2, table counts can diverge considerably from the expectations based on fixed P 1 when P 1 is in fact not fixed.

(7) can be viewed as an approximation in this case, but not necessarily an accurate one.


In addtion, a lossless approach can easly combined with pruning.

lossless representation of N -gram is a key issue even for lossy approaches.


We assign a word id for each   Table 2: Compression ratio for pointers word according to its reverse sorted order of frequency.

highly frequent words are assigned smaller values, which in turn occupies less space in our variable length coding.


The most frequently observed count in N -gram data is one.

we can reduce the space by the variable length coding.


The logprobability spent on the zero suffixes in the split+0 dataset has to be spent on trying to decide whether to include a stem or suffix following a stem in the split dataset.

the difference in total logprobability between the two datasets is small (only 6% perplexity difference).


The lower-order models used in KN smoothing, however, are very poor estimates of the probabilities for N-grams that have been observed in the training corpus, so they are Figure 1: General language model smoothing schema not suitable for use in coarse-to-fine search.

two versions of every language model below the highest-order model would be needed to use KN smoothing in this case.


As we have noted, most existing interpolation models are constrained so that the discount parameters fully determine the interpolation parameters.

the discount parameters have to correct for both types of error.


And it was not clear how the errors of topic clustering techniques affected the inference scheme.

it will be valuable to design inference methods for more fine-grained events.


Here later information may override earlier more tentative or incomplete events.

different events with particular types tend to occur together frequently, for example, the chains of "Conflict Life-Die/Life-Injure" and "Justice-Convict Justice-Charge-Indict/Justice-Trial-Hearing" often appear within one document.


must be balanced against any resulting increase in the computational cost of the parsing algorithm.

recent work in dependency parsing has been restricted to applications of secondorder parsers, the most powerful of which (Carreras, 2007) requires O(n 4 ) time and O(n 3 ) space, while being limited to second-order parts.


A dependency relationship is a boolean value that represents whether this word pair forms a dependency edge.

a set of classification instances are obtained.


On the other hand, there are as many as 2|f |(|f |−1) candidate projected dependency instances for the target sentence f .

we need choose a threshold b for C p (i, j) to filter out the ambiguous instances: the instances with C p (i, j) > b are selected as the positive, and the instances with C p (i, j) < 1 − b are selected as the negative.


As described previously, the score of a dependency tree given by a word-pair classifier can be factored into each candidate dependency edge in this tree.

the projected classifier can be integrated with a baseline model deeply at each dependency edge, if the evaluation score given by the baseline model can also be factored into dependency edges.


On the training method, however, our model obviously differs from other graph-based models, that we only need a set of word-pair dependency instances rather than a regular dependency treebank.

our model is more suitable for the partially bracketed or noisy training corpus.


we can use the information on the Chinese side to help disambigua-He ate the meat with a fork .


we have two target parts "W 3:0-W 1:1" and "W 3:2-W 1:0".


(2008) can be seen as a special case of our construction.

our system can serve as a general framework for removing unintended readings from an underspecified representation.


If M R reads u in stateq, this means that M R will copy the subtree below u unchanged because the rewriting has taken place elsewhere.


every girl kissed a boy entails every blond girl kissed someone.

ann(every u , a, 1) = −a and ann(every u , a, 2) = a (where u is a node name as above).


Annotation guidelines The ACE-2 annotation guidelines describe generic NPs as referring to an arbitrary member of the set in question, rather than to a particular individual.

a property attributed to a generic NP is in principle applicable to arbitrary members of the set (although not to all of them).


The classification of generic NPs has proven difficult even for humans.

a machine learning approach seemed promising, both for the identification of relevant features as for capturing contex- Figure 1: A decision tree trained on feature Set 5 tual factors.


We call this form of semantic knowledge as structural semantic knowledge, or implicit semantic knowledge.

in order to deduce a reliable semantic relatedness measure, we must take both the edges and the structure of semantic-graph into consideration.


This definition is recursive, and the starting point we choose is the semantic relatedness in the edge.

our structural semantic relatedness has two components: the neighbor term of the previous recursive phase which captures the graph structure and the semantic relatedness which captures the edge information.


The strategies were never mixed (with some jargon, some descriptive and some tutorial expressions) within a single conversation.

please note that the strategies used for data collection were not adaptive and the human 'wizard' has no role in choosing which referring expression to present to the user.


We assume that users can interpret the descriptive expressions and resolve their references.


All the policies exploit the user model in subsequent references after the user's knowledge of the expression has been set to either yes or no.

although these policies are simple, they do adapt to a certain extent, and are reasonable baselines for comparison in the absence of expert knowledge for building more sophisticated baselines.


Intuitively, when a given sentence is more dissimilar from most of the other sentences, it may incur higher loss as it is taken as the representative sentence (or summary sentence) to represent the main theme embedded in the other ones.

the loss function can be built on the notion of the similarity measure.


Large volumes of primary texts will be useful even without translation for such tasks as language modeling and unsupervised learning of morphology.

we anticipate that the corpus will have the usual "pyramidal" structure, starting from a base layer of unannotated text, some portion of which is translated into a reference language at the document level to make the next layer.


Dictionaries published by a single publishing house tend to partition the semantic fields of headwords in the same way.

the first translation of some English headword in the English-Spanish and in the English-Hebrew dictionaries would correspond to the same sense of the headword, and would therefore constitute translations of each other.


We define the signature G(w) N,k of w to be the set of N words with the highest PMI with w. Note that a word's signature includes words in the same language.

two signatures of words in different languages cannot be directly compared; we compare them using a lexicon L as explained below.


While it measures how well the sets of words that tend to co-occur with these two words align to each other, its strength may lie in bypassing the question of which word in one language should be aligned to a certain context word in the other language.

unlike other scoring methods, it is not effected by incorrect alignments.


A great gap exists between language processing of automatic evaluation and the processing by humans.

in recent years, various automatic evaluation methods particularly addressing sentence-level automatic evaluations have been proposed.


In the LCS route selected using our method, the weight of "the" in "the amount of" is 2 because "the" in MT output and "the" in the reference are included in the corresponding noun phrase "NP1".

the system based on our method can select the correct LCS route.


The noun phrase "it", which has no corresponding noun phrase, is expressed as "NP" in the reference.

the MT output is generalized as "NP1 NP2 NP3"; the reference is generalized as "NP NP3 NP1 NP2".


The system has made an arbitrary choice to retain the Lookup annotation and discard the Token annotation.

no Caps annotations are output by phase P 1 .


Matching priority causes the grammar to skip the corresponding match for "Scott" as a Last.

phase P 2 fails to identify "Mark Scott" as one single Person.


In Barzilay and Lapata (2008), an entity grid is constructed for each document, and is represented as a matrix in which each row represents a sentence, and each column represents an entity.

a cell in the matrix contains information about an entity in a sentence.


Furthermore, there are additional constraints on constituent order related to pronominalization.

we devised additional entity representations to account for these aspects of German.


Another instance of syntactic prediction has been reported by Staub and Clifton (2006): following the word either, readers predict or and the complement that follows it, and process it faster compared to a control condition without either.

human language processing takes advantage of the constraints imposed by the preceding semantic and syntactic context to derive expectations about the upcoming input.


Third, resources for non-English languages often have a much poorer coverage since the construction effort must be repeated for every language of interest.

an obvious bias exists towards conducting research in resource-rich languages, such as English.


We estimate p(s, w) as: where score(s, w) = |Ctx(s) ∩ Ctx(w)| + 1 (we add 1 as a smoothing factor).

in our algorithm we determine the best sense s by computing the intersection of the disambiguation contexts of s and w, and normalizing by the scores summed over all senses of w in Wikipedia and WordNet.


We translate these sentences with the Google Translate API and select the most frequent translation in each language.

we can enrich the initial babel synset with the following words: mongolfière FR , globus CA , globo ES , mongolfiera IT .


Secondly, while Wikipedia is less structured than WordNet, it provides large amounts of semantic relations and can be leveraged to enable multilinguality.

even when they overlap, the two resources provide complementary information about the same named entities or concepts.


Adjuncts tend to belong to one of a few specific semantic domains (see Section 2).

if an argument tends to appear in a certain slot in many of its instances, it is an indication that this argument tends to have a consistent semantic flavor in most of its instances.


In fact, we could output more than 500 words if we ignored the cost of storage.

we also included the results for the case where L = M = 3600 and N = 2, 000.


Recent studies have pointed out that the Bayesian framework derives state-of-the-art smoothing methods such as Kneser-Ney smoothing as a special case (Teh, 2006;Mochihashi et al., 2009).

it is reasonable to resort to the Bayesian framework.


Inten-tion and concerns of active users may change as the discussion goes on.

recommendation, if it were only based on the original posting, can not benefit the potentially evolving interests of the users.


In a discussion thread, comments made at different levels reflect the variation of focus of readers.

recommended articles should reflect their concerns to complement the author's opinion.


Similarly, we can also use a | | × | | matrix defined on [0, 1] to record the content similarity between nodes and denote it by .

we combine these three aspects linearly: The importance of a text node can be quantized by the times it has been referred to.


This can be captured by a | | × | | matrix of {0, 1}, whose entries are denoted by , .

with this information and assuming poster has made a comment k for user 's posting, the final weight of this comment is defined as Once the weight of comments on one posting is quantified by our models, this information along with the entire discussion thread is fed into a synthesizer to construct a topic profile.


• If is said to be more general than , then the additional keyword, 2 , of article must render less constrained than .

the content of can be interpreted as 1 ∪ 2 .


The recommendation engine may return a set of essentially the same articles re-posted at different sites.

we introduce a metric of novelty to measure the topic diversity of returned suggestions.


(2007) reported that 16.5% of valid search terms do not occur in their 200K-entry spelling lexicon.

recent research has focused on the use of Web corpora and query logs, rather than human-compiled lexicons, to infer knowledge about misspellings and word usage in search queries (e.g., Whitelaw et al., 2009).


Unfortunately, we found in our experiments that the pairs extracted using the method are too noisy for reliable error model training, even with a very tight threshold, and we did not see any significant improvement.

in Section 6 we will not report results using this dataset.


Furthermore, the word-level alignments between Q and C can most often be identified with little ambiguity.

we restrict our attention to those phrase transformations consistent with a good word-level alignment.


Our work, however, focuses on classification of NP instances in context, so the same phrase may be assigned to different semantic classes in different contexts.

our classifier can also assign semantic class labels to pronouns.


Consequently, this approach can produce highly imbalanced training sets.

we enforced a 3:1 ratio of negatives to positives by randomly selecting a subset of the possible negative instances.


Like Kylin, LUCHS creates training data by matching Wikipedia attribute values with corresponding sentences, but by itself, this method was insufficient for accurate extraction of most relations.

lUCHS introduces a new technique, dynamic lexicon features, which dramatically improves performance when learning from sparse data and that way enables scalability.